SEO Title: GLM vs Qwen vs DeepSeek: 5-Month Local LLM Benchmark on Function Calling

What Is the Local LLM Benchmark on Function Calling?

The Local LLM Benchmark on Function Calling is a recent comparison study designed to evaluate and contrast three popular Large Language Models (LLMs): Google’s GLM, Hugging Face’s Qwen, and DeepSeek’s DeepSeek 3.5B. This benchmark focuses specifically on their performance in backend generation tasks using function calling, providing insights into efficiency, accuracy, and cost-effectiveness for open-source projects.

This study is significant because it offers a controlled environment to assess how these models perform in real-world scenarios, particularly when generating backend code. Unlike previous comparisons that lacked structured controls, this benchmark introduces standardized variables and metrics, ensuring more reliable and actionable results.

Why This Benchmark Matters for Cost and Efficiency

The Local LLM Benchmark on Function Calling is particularly relevant for developers and organizations seeking to optimize their use of AI models in backend systems. With the increasing popularity of open-source projects, cost-effectiveness has become a critical factor in model selection.

This benchmark highlights the trade-offs between frontier models like Google’s GLM, which can be prohibitively expensive due to their high computational requirements (e.g., $1,000 per deployment), and more accessible models like Qwen and DeepSeek. The study shows that smaller, scaled-down models may offer comparable performance while being more cost-effective for open-source projects.

Additionally, the benchmark provides insights into how these models can be integrated into backend systems to improve efficiency. By comparing their function calling capabilities, developers can make informed decisions about which model best suits their specific needs without compromising on performance or budget constraints.

How the Controlled Version of the Benchmark Works

The controlled version of this benchmark differs significantly from its predecessor by introducing systematic variables and a scoring rubric. While the initial uncontrolled benchmark lacked standardized controls, this latest iteration ensures that all models are evaluated under similar conditions, minimizing external factors that could skew results.

Participants in the benchmark submitted their function calling code for evaluation based on predefined criteria, such as accuracy, efficiency, and adherence to best practices. A panel of experts then scored each submission using a consistent rubric, ensuring fairness and reliability in the comparison.

Real-World Use Cases for the Backend Generation Comparison

The findings from this benchmark have practical implications for developers working on backend systems that rely on function calling for tasks like backend agent functionality or customer support automation. For instance, an open-source project managing a ticketing system could benefit from selecting an LLM that efficiently generates and executes backend functions to handle user requests.

Another use case involves enterprises looking to optimize their AI-driven applications without the high upfront costs of frontier models. By comparing Qwen and DeepSeek’s performance against GLM in controlled scenarios, organizations can identify which model best balances cost and functionality for their specific application needs.

GLM, Qwen, and DeepSeek Compared: Key Findings from the Benchmark

The benchmark reveals several key insights into the relative strengths of GLM, Qwen, and DeepSeek when it comes to function calling in backend generation.

1. GLM vs Qwen: Google’s GLM demonstrated strong performance in terms of raw computational power but was significantly more expensive than Qwen. The study found that Qwen 3.5-35B-a3b showed promise in matching the functionality and efficiency of GLM, suggesting it as a viable alternative for cost-sensitive projects.

2. GLM vs DeepSeek: While DeepSeek’s models were not explicitly compared to GLM in detail, the benchmark highlighted their potential scalability and performance in specific tasks. DeepSeek’s architectures seemed well-suited for certain function calling scenarios, making them attractive options for niche applications.

3. Model Performance: The study found that all three models performed similarly in controlled environments, with slight variations depending on the specific task or function being tested. This suggests that model selection may come down more to organizational preferences or specific project requirements than fundamental performance differences.

Common Pitfalls to Avoid When Choosing LLM Models for Function Calling

Selecting the right LLM for function calling in backend systems involves balancing several factors, including cost, scalability, and performance. Here are some common pitfalls to avoid:

1. Overlooking Cost: Frontier models like GLM can be prohibitively expensive, making them unsuitable for many open-source or budget-sensitive projects. Developers should carefully evaluate whether the increased performance justifies the added cost.

2. Relying Solely on Frontier Models: While frontier models excel in raw computational power, they often lack the scalability needed for large-scale backend systems. Organizations should consider how well these models can adapt to future growth or complexity demands.

3. Ignoring Function Calling Specifics: Not all LLMs are equally suited for function calling tasks. Developers must ensure that the model selected aligns with the specific requirements of their backend functions, such as speed, accuracy, and integration compatibility.

Frequently Asked Questions

1. What models are included in the benchmark comparison?
   The benchmark includes Google’s GLM, Hugging Face’s Qwen 3.5-35B-a3b, and DeepSeek’s 3.5B models for function calling tasks.

2. Will the benchmark include comparisons with other LLMs in the future?
   While this benchmark focuses on GLM, Qwen, and DeepSeek, the findings will likely inform broader discussions about model selection across the AI tools ecosystem. Future benchmarks may expand to include additional models based on user demand.

Sources

• Local LLM Benchmark about Backend Generation by Function Calling (GLM vs Qwen vs DeepSeek) — r/LocalLLaMA

Frequently Asked Questions

What exactly does the Local LLM Benchmark on Function Calling evaluate?

The benchmark evaluates the performance of Google’s GLM, Hugging Face’s Qwen, and DeepSeek’s DeepSeek 3.5B in backend generation tasks using function calling, focusing on efficiency.

Why is function calling significant for these LLMs?

Function calling allows models to perform complex reasoning and handle various programming tasks, making it crucial for assessing their capabilities in dynamic applications.

Where can someone find detailed results of this benchmark comparison?

The benchmark provides detailed insights into the comparative performance of GLM, Qwen, and DeepSeek 3.5B based on function calling tasks, offering practical guidance for model selection.

How do GLM, Qwen, and DeepSeek compare in terms of function calling efficiency?

The study highlights specific performance metrics such as speed, accuracy, and scalability differences between the three models when performing function calls.

What are some real-world applications that benefit from these LLMs' function calling capabilities?

Applications like automated scripting, data processing, and intelligent chatbots can benefit from these models' efficient function calling abilities, enhancing task automation in various industries.